Liquid AI has launched the LFM2.5-350M, an enhanced version of its 350M model, featuring 28 trillion tokens of pre-training and improved performance in data extraction and tool use. The model runs efficiently on various hardware, making it suitable for large-scale data pipelines and edge deployments.

Organizations are increasingly faced with the decision of whether to implement Retrieval-Augmented Generation (RAG) or fine-tuning for their AI initiatives. RAG connects large language models to external databases, allowing access to real-time information, reducing inaccuracies, and enhancing security and traceability. However, implementing RAG comes with its own technical challenges that require careful planning and maintenance.

Deep Atlas offers an intensive curriculum designed to compress months of AI and machine learning education into just weeks. With hands-on projects, community learning, and successful alumni, participants can quickly gain the skills needed for a career in AI.

PostHog AI has evolved significantly over its first year, transforming from a basic tool to a comprehensive AI agent capable of complex data analysis and task execution. Key learnings highlight the importance of model improvements, context, and user trust in AI interactions. The platform is now utilized by thousands weekly, offering insights into product usage and error management.

Foundation models in pathology are failing not due to size or training duration but because they are built on flawed assumptions about data scalability and generalization. Clinical performance has plateaued, as models struggle with variability across institutions and real-world applications, highlighting a need for task-specific approaches instead of generalized solutions. Alternative methods, like weakly supervised learning, have shown promise in achieving high accuracy without the limitations of foundation models.